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| Section | Weight | Objectives |
|---|---|---|
| Describe features of generative AI workloads on Azure | 10-15% | - Generative AI concepts
|
| Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Azure NLP services
|
| Describe Artificial Intelligence workloads and considerations | 20-25% | - Identify features of common AI workloads
|
| Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
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NEW QUESTION # 99
What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave.
Option A is correct because instructions can define constraints the model must follow.
Option B is correct because instructions can define the agent's role and behavior.
Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions.
NEW QUESTION # 100
You deploy an Al system to assist with hiring decisions.
Company policy requires that human reviewers oversee Al-generated recommendations and remain responsible for final hiring decisions.
Which Microsoft responsible Al principle is this an example of?
Answer: A
NEW QUESTION # 101
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: The Temperature parameter can be set before deploying a model. = No temperature is an inference/request parameter used when calling or testing a deployed model. It controls randomness in generated responses. It is not a required setting for deploying the model itself.
Statement 2: During inference, the model name is used to route requests to a specific deployment. = No In Azure OpenAI / Microsoft Foundry deployments, application requests are routed to a specific deployment name , even when the SDK parameter is called model. The underlying model name, such as gpt-4.1-mini, is not what routes the request to the deployment.
Statement 3: After a model is deployed, both code and testing tools can be used to interact with the model. = Yes After deployment, you can test the model in Foundry playground/testing tools or call the deployment from application code by using the endpoint, deployment name, and authentication credentials.
NEW QUESTION # 102
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 103
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following table.
Which type of natural language processing was performed?
Answer: B
Explanation:
The output identifies text values and labels them with entity categories, such as:
now [DateTime]
students [PersonType]
Microsoft [Organization]
175 [Quantity-Number]
Today [DateTime-Date]
This is entity recognition, also known as Named Entity Recognition (NER). NER identifies and categorizes entities in unstructured text, such as people, organizations, locations, dates, quantities, numbers, and other named or structured values.
Why the other options are incorrect:
B). translation would convert text from one language to another.
C). key phrase extraction would return important phrases or main topics, not categorized entity labels.
D). sentiment analysis would return sentiment such as positive, negative, neutral, or mixed.
NEW QUESTION # 104
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